title: Multitask learning for host-pathogen protein interactions.
abstract: BACKGROUND
  An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host-pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called 'multitask learning', which considers the problem of building models across tasks that are related to each other. A 'task' in our scenario is the set of host-pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.
  
  
  RESULTS
  Our current work on host-pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex-Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host-pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.
  
  
  BACKGROUND
  The predictions and code are available at: http://www.cs.cmu.edu/∼mkshirsa/ismb2013_paper320.html .
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA.
authors.forename: Meghana
authors.initials: M
authors.lastname: Kshirsagar
authors.forename: Jaime
authors.initials: J
authors.lastname: Carbonell
authors.forename: Judith
authors.initials: J
authors.lastname: Klein-Seetharaman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000378:metabolism*; D054884:Host-Pathogen Interactions; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*
chemical_list: D001426:Bacterial Proteins